This paper proposes a stochastic model for mixed traffic flow with human-driven vehicles
(HVs) and automated vehicles (AVs). The model is formulated in Lagrangian coordinates …

Freeway traffic state estimation and prediction are central components in real-time traffic
management and information applications. Model-based traffic state estimators consist of a …

This book shows the history of traffic flow modelling from the perspective of modern day
applications. Traffic flow models describe how vehicles travel over roads, at which speeds …

This paper proposes a method of estimating a traffic state based on probe vehicle data that
contain spacing and position of probe vehicles. The probe vehicles were assumed to …

In real-time traffic management and intelligent transportation systems (ITS) applications, an
accurate picture of the prevailing traffic state in terms of speeds and densities is critical, for …

In this paper we build Lagrangian continuum traffic flow models that are able to utilize
trajectory information transmitted between connected vehicles via vehicle-to-everything …

The objective of this paper is to present a new analytical tool that predicts highway
congestion in real time by utilizing a macroscopic traffic flow model, and to investigate a data …

Observability is essential to estimate the states of a system from sensor measurements.
Observability indicates the sufficiency of the sensors for state estimation. In a highway traffic …

Recent researches have shown the potential benefits of using Lagrangian coordinates in
modeling mobile sensor data such as GPS, Bluetooth, Wi-Fi, and cellphone probe data …

Fastlane, a generic multiclass kinematic wave traffic flow model, is proposed and analyzed.
The model takes into account heterogeneity of driver-vehicle units with respect to speed and …